Method and device for identifying the state of a system for effecting the automatic longitudinal and/or lateral control of a motor vehicle

ABSTRACT

Method and device for state estimation in a system for automatic longitudinal and/or transverse regulation in a motor vehicle, operating according to the radar principle and/or the lidar principle, in particular for detecting soiling and/or blindness of a sensor is disclosed. The state estimation is dependent upon at least two indicators, which are formed from the signals received and/or transmitted by the sensor.

FIELD OF THE INVENTION

The present invention relates to a method and a device for stateestimation in a system for automatic longitudinal and/or transverseregulation in a motor vehicle. Such systems are used, for example, aspart of an automatic cruise control in a vehicle for detection ofvehicles driving ahead. In these systems (e.g., adaptive cruisecontrol), the traditional cruise control is supplemented by a distancesensor, so it is possible to automatically adjust the speed and/ordistance to the traffic situation ahead of the vehicle. To guaranteereliable use in such a system, the function of the system is usuallymonitored.

BACKGROUND INFORMATION

German Patent Application 196 44 164 A1 describes a motor vehicle radarsystem in which a dielectric body is positioned in the path of the beamof the electromagnetic waves for protection against weather effects andpreferably also for focusing. To detect and possibly eliminate dirt andcoatings of ice, snow or moisture deposited on this dielectric body, thedielectric body has an arrangement of electric conductors. With theseconductors, the dielectric body may be heated, the attenuation caused bya possible coating may be measured and a target simulation may beperformed for function testing of the radar system. To measure a coatingof ice, snow or moisture with this automotive radar system, thedielectric body is covered by two chamber-like arrangements that aretightly interleaved with one another without coming in contact. Each ofthe chamber-like arrangements is a separate, electrically connectedstructure. A resistance R and a capacitance C may be measured betweenthese two interleaved arrangements. These values depend on the lossangle tan δ of the material between the two arrangements and thus alsoon loss angle tan δ of any coating that may be present. In this way, itis possible to determine the signal attenuation caused by a coating andthus the degree of soiling. To perform this measurement of the coating,the electrically conducting arrangement must be applied to the outsideof the dielectric body.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method and a devicefor state estimation in a system for automatic longitudinal and/ortransverse regulation in a motor vehicle, so that soiling and/orblindness of a sensor will be determined reliably, rapidly,inexpensively and preferably without additional hardware components.

According to the present invention, this object is achieved by the factthat in a method for state estimation in a system for automaticlongitudinal and/or transverse regulation in a motor vehicle, operatingaccording to the radar principle and/or the lidar principle, inparticular for detecting soiling and/or blindness of a sensor, the stateestimation relies on at least two indicators (I_(n)) formed from thesignals received and/or transmitted by the sensor.

This implementation offers the advantage that it does not require anyadditional hardware components, in particular no additional electricconductors on the outside of the sensor, for detection of soiling and/orblindness of a sensor. This is an inexpensive implementation of themethod according to the present invention. Instead, the object isachieved via at least two indicators (I_(n)) formed directly from thesignals received and/or transmitted by the sensor. Thus, rapid andreliable detection of soiling and/or blindness of a sensor is possibledirectly when the system having the sensor is put in operation and a fewmeasurements have been performed, yielding analyzable results.

The at least two indicators (I_(n)) are preferably weighted withweighting factors (a_(n)). This offers the advantage that the indicatorsare weighted according to their different importance for the state ofthe system.

It is particularly advantageous to link the weighted indicatorstogether. This offers the advantage of forming a linked indicator whichhas a greater relevance with regard to the stated object than anindividual indicator (I_(n)).

For the transparency and comparability of the resulting data, it isadvantageous for the sum of the weighting factors (a_(n)) to be nogreater than 1.

It is particularly advantageous that the weighting factors (a_(n))depend on at least one driving situation (F_(n)) and/or one probability(V_(n)) which is to be determined. This offers the advantage that themethod according to the present invention may be adapted flexibly tovarious driving situations (F_(n)) and/or to probabilities (V_(n)) thatare to be determined. It is advantageous here to take into account atleast one of the following driving situations (F_(n)) in determinationof the state (P) of the system:

1. The system detects another motor vehicle which is used as the targetobject for regulation.

2. The system does not detect any possible target object, but the systemdoes detect other moving objects.

3. The system does not detect either a possible target object or anyother moving objects.

By taking into account at least one of the aforementioned drivingsituations (F_(n)), it is possible to classify the driving situations(F_(n)) as they are typically differentiated by a system of the generictype.

As probabilities (V_(n)) to be determined, at least one of the followingprobabilities (V_(n)) is selected in the method according to the presentinvention:

1. The performance of the system is optimal.

2. The performance of the system is not optimal.

3. No functioning of the system is possible.

By determining at least one of the aforementioned probabilities (V_(n)),a probability (V_(n)) which unambiguously describes the state (P) of thesystem is advantageously determined.

Consequently, it is especially advantageous that the linked indicatorsyield at least one probability (V_(n)) which makes a statement regardingthe probable state (P) of the system. The method is advantageously setup in such a way that the greatest of the probabilities (V_(n))describes the state (P) of the system. This yields a state (P) of thesystem which supplies information regarding the probable state (P) ofthe system in a single quantity.

It is also advantageous that, as part of the method according to thepresent invention, the indicators (I_(n)) are normalized so that thepossible value varies in a range between 0 and 1. This offers theadvantage that all the indicators (I_(n)) are within one and the samevalue range, thus facilitating a comparison or an evaluation of theindividual indicators (I_(n)). In particular in conjunction with theaforementioned interpretation of the weighting factors (a_(n)) such thatthe sum of the weighting factors (a_(n)) is no greater than 1, thisoffers the possibility that the resulting probabilities will also varyin a possible value range between 0 and 1 and will thus directlyindicate a probability (V_(n)) of the system.

It is particularly advantageous that at least one of the followingindicators (I_(n)) is used in the method according to the presentinvention:

1. The average angle quality of all objects detected by the system,which permits a statement regarding the quality of the object angledetermined.

2. The object stability, which describes the rate of detection failuresof the target or control object selected for the vehicle's longitudinalregulation.

3. The average power of the signals received by the sensor.

4. The sum of all objects detected by the system during a measurement.

5. The linkage of the distance and amplitude of the object detected atthe greatest distance.

6. The road surface reflection detected by the system.

The indicators (I_(n)) listed above offer the advantage that they havedifferent degrees of prominence depending on different drivingsituations (F_(n)). In this way it is possible to make a statementregarding the probable state (P) of the system in almost any drivingsituation (F_(n)).

In addition, in the method according to the present invention, it hasproven advantageous that a state (P) is assumed to be determined onlywhen the result of the linked indicators is obtained for a predeterminedperiod of time (T). This offers the advantage that short-term changes inprobabilities (V_(n)) due to external influences, for example, do nothave a direct effect on the state (P), which is assumed to bedetermined.

It is advantageous that there are transition states between theprobabilities (V_(n)) and these transition states are also used foranalysis. For the case when there is a transition state between twoprobabilities (V_(n)), the resulting data may also be used for analysis.

It is a particular advantage of the method according to the presentinvention that the state (P) of the system thus determined is sent to acontrol unit which in turns controls a cleaning device for the sensor.This cleaning device is preferably at least a device that works withwater, a mechanical cleaning device or a heater for the sensor. Thisinfluence on the control of a cleaning device for the sensor offers theadvantage that cleaning of the sensor is optimized. In this way, thecleaning device for the sensor may be controlled, for example, in casesin which the control unit would not otherwise have triggered thecleaning device for the sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an automotive radar system into which the method accordingto the present invention is integrated.

FIG. 2 shows a schematic diagram of the sequence of the method accordingto the present invention.

FIG. 3 shows an exemplary diagram of the resulting transitional states.

FIG. 4 shows a possible arrangement to cause the determined state (P) ofthe system to be sent to a control unit which in turn triggers acleaning device for the sensor.

DETAILED DESCRIPTION

FIG. 1 shows an automotive radar system having a housing 1 and afocusing means 2, which may be, for example, a dielectric lens. Housing1 contains a base plate 3 on which is situated a printed circuit board4. On printed circuit board 4 are mounted electronic components 5,including the device according to the present invention for stateestimation in a system for automatic longitudinal and/or transverseregulation in a motor vehicle.

Radiating elements 6, which may be designed as patch antenna elements 6,for example, are also mounted on circuit board 4. High-frequencymicrowave radiation is transmitted and/or received over these radiatingelements 6. This radiation is prefocused by prefocusing means 7 beforebeing finally focused by focusing means 2. In the direction ofreception, the sequence is reversed accordingly. Prefocusing means 7 areheld in position above radiating elements 6 by a cover 8.

It is also within the scope of the method according to the presentinvention that this method is employed in a radar system of any type,which is based on electromagnetic microwave radiation and/or based onthe lidar principle.

FIG. 2 shows a schematic diagram of the sequence in the method accordingto the present invention. In a state determination unit 9, probabilities(V_(n)) are determined in general case n.

The probabilities (V_(n)) to be determined are used to determine theprobable state (P) of the system.

In the context of this embodiment, the following may be used, forexample, as probabilities (V_(n)) to be determined, the greatest thenbeing determined as possible state (P) of the system, for example:

1. The performance of the system is optimal (V₁). This may indicate, forexample, that the sensor of the system is free of any soiling.

2. The performance of the system is not optimal (V₂). This may mean, forexample, that the sensor of the system is soiled.

3. No functioning of the system is possible (V₃). This may mean, forexample, that the sensor of the system is blind.

This definition of the probabilities (V_(n)) to be determinedcorresponds to the embodiment presented here. It is of course alsopossible to expand or further restrict the number of probabilities(V_(n)) to be determined. This depends primarily on the purpose forwhich the state signal (P) thus determined is to be used further on, andwhether a qualitative result (function/no function) or a quantitativeresult (exact state value) is to be obtained.

Indicators (I_(n)) 10 are made available to state determination unit 9for determination of the probabilities (V_(n)), as discussed in greaterdetail below. In this embodiment, the number of indicators (I_(n)) isgenerally assumed to be n. An instantaneous driving situation/drivingstate (F_(n)) 11 is transmitted to state determination unit 9. In thisembodiment, n different driving situations (F_(n)) are indicated ingeneral. The possible driving situations (F_(n)) will also be discussedin greater detail below. Depending on which probability (V_(n)) is to bedetermined at a given time in state determination unit 9, and dependingon the prevailing driving situation (F_(n)) 11, the correspondingweighting factors (a₁ through a_(n)) are selected from a weightingfactor table 12. This weighting factor table 12 contains the respective,suitably adapted weighting factors (a₁ through a_(n)) for each possiblecombination of probability (V_(n)) and possible driving situation(F_(n)) to be determined. The corresponding selected weighting factors(a₁ through a_(n)) are multiplied by the corresponding indicators(I_(n)) in state determination unit 9. The indicators weighted in thisway are added up in state determination unit 9, yielding the probability(V_(n)) to be determined. In general, for linking the indicators (I_(n))and the weighting factors (a₁ trough a_(n)), any desired form of linkagewhich yields a relevant result is possible. This procedure is repeatedfor each probability (V_(n)) to be determined. Due to the fact that boththe indicators and the weighting factors (a_(n)) are normalized to avalue range between 0 and 1, this yields on the whole probabilities(V_(n)) which are also in the value range between 0 and 1. In the caseof the indicators (I_(n)), a value of 0 denotes completesoiling/blindness of the sensor and 1 denotes no soiling of the sensor.In the case of the probabilities (V_(n)) to be determined, 1 denotesthat the sensor is 100% free and 0 denotes that the sensor is 0% free.After all probabilities (V_(n)) to be determined have been determined bystate determination unit 9, the probability (V_(n)) which is thegreatest is selected. In particular when the probability (V_(n))determined in this way signals a poor state (P) of the system, a checkis performed using a timing element 13 to determine whether theprobability determined is the greatest of the probabilities (V_(n)) tobe determined at least for a period of time (T) according to timingelement 13. If this is the case, i.e., one of the probabilities (V_(n))to be determined was the greatest of the probabilities (V_(n)) to bedetermined for at least a period of time (T), then this probability(V_(n)) is selected as the probable state (P) of the system and is madeavailable as a state (P) of sensor 14 for further processing.

The advantage in using weighting factors (a_(n)) in particular is thatit is possible to adapt to almost any possible driving situation (F_(n))and/or probability (V_(n)) to be determined. The number of weightingfactors to be stored in the table depends on the number of differentdriving situations, the number of different probabilities to bedetermined and the number of indicators used. In the simplest case oftwo driving situations, one probability to be determined, and twoindicators, a total of 2×1×2=4 weighting factors are thus needed. In amore complicated embodiment having, for example, three drivingsituations, three probabilities to be determined, and six indicators, atotal of 3×3×6=54 weighting factors would be necessary. The choice ofthe combination used will depend on many factors, such as, the sensorsystem used or the required accuracy and versatility, and this is up tothe judgment of those skilled in the art.

The driving situations (F_(n)) mentioned above should be adapted to theavailable sensor system. Depending on how sensitive the sensor is andhow many and which driving situations (F_(n)) are to be differentiablewith the sensor, the driving situations to be taken into account shouldbe determined. The most important driving situation to be taken intoaccount in any case is when the system detects a different vehicle,which is selected as the control object for a longitudinal and/ortransverse regulation. It is relevant to take into account this drivingsituation (F_(n)) because a system for automatic longitudinal and/ortransverse regulation in a motor vehicle in the mode in which it hasselected a target object for regulation will usually supply the mostreliable and most accurate data. Another driving situation (F_(n)) to betaken into account is the driving situation (F_(n)) in which the systemdoes not detect any possible object for regulation but does detect othermoving objects/target objects. These may be, for example, vehiclescoming from the opposite direction, moving objects at the edge of theroad or vehicles/target objects ahead of the regulated vehicle but notselected as the regulation object (because, for example, they aretraveling in a different lane). In addition, a driving situation (F_(n))in which the system does not detect a possible target object forregulation or any other moving objects/target objects is also possible.Within the scope of the present invention, all driving situations(F_(n)) which may be differentiated by the system are also conceivable.

One of the most important points with regard to the reliability of themethod according to the present invention is the selection of theindicators (I_(n)) used, which together with the weighting factors(a_(n)) represent the calculation basis of the method. As part of thisembodiment, the following six possible indicators (I_(n)) will now bepresented. However, other indicators (I_(n)) which may be formed fromthe signals received or sent out by the sensor are also conceivable. Themost important criterion of such an indicator (I_(n)) is that it changesat least as a function of the state (P) of the sensor.

A first possible indicator (I_(n)) is the average angle quality of allobjects detected by the system. The angle quality may be determined, forexample, from the quotient of the real object angle and the differencebetween the real object angle and the measured object angle. To obtain arelevant indicator (I_(n)) which provides information regarding thequality of the object angle determined, the angle quality of alldetected objects is determined. To be able to make this indicator(I_(n)) comparable or linkable to the other indicators (I_(n)) whichfollow, the indicator (I_(n)) is normalized to a value range between 0and 1. In the case of a radar system based on electromagnetic microwaveradiation, it is advisable to consider the angle qualities only in arange in which high qualities are generally expected. This is an anglerange of approx. ±3°, for example, with a radar system for automaticcruise control and distance regulation. If the average angle qualitythen breaks down in this angle range, soiling and/or blindness of thesensor system may be assumed with a high probability. If the sensorsystem does not detect any object, e.g., because of total blindnessand/or absolute free passage, then this indicator (I_(n)) becomes lessimportant. The different degrees of importance of the indicators (I_(n))is achieved in the method according to the present invention byweighting factors (a_(n)) adapted to the corresponding driving situation(F_(n)). In this way, the indicators having a high relevancespecifically in the given driving situation (F_(n)) will be weightedmore heavily in the method according to the present invention in anydriving situation (F_(n)). In the case of the present indicator (I_(n))of average angle quality of all objects detected by the system, thiswould be, for example, the driving situation (F_(n)) in which the systemhas selected a vehicle/target object in front of the regulated vehicleas the object for regulation (regulated following).

Another possible indicator (I_(n)) is the object stability, whichdescribes the rate of detection failures of the target object orregulation object selected for the vehicle longitudinal regulation. Forexample, if a system for automatic cruise control and distanceregulation in a motor vehicle has selected a vehicle traveling ahead ofit as the regulation object, then tracking is implemented. In thistracking, the data determined by the sensor system in each time intervalis compared with that detected in the previous time intervals. In thismanner, a type of virtual track may be stored in the memory of thesystem for each object detected. It usually occurs here that even theselected regulation object is not detected in each time interval. Thisresults in detection gaps in the virtual track. The object stability maythen be determined from the quotient of the number of time intervals inwhich an object has been detected and the total number of time intervalsin which the object has been observed. This indicator (I_(n)) is alsonormalized to a value range between 0 and 1 to make it comparable to theother indicators. If this indicator (I_(n)) is filtered, normalized andconsidered over a longer period of time, it is very sensitive to analtered system state, in particular when there is a regulation objecttraveling ahead of the regulated vehicle, or when a target object hasbeen selected for regulation; this is because, in general, a very highobject stability is expected in this driving situation (F_(n)).Precisely this situation in which one's own vehicle is traveling behinda regulation object traveling ahead is one of the situations in whichthe state (P) of the system is often greatly impaired. This may occur,for example, due to the fact that dirt, snow or slush is thrown up bythe regulation object traveling ahead, soiling the sensor of one's ownsystem for automatic cruise control and/or distance regulation. For thisreason, the indicator “object stability” is weighted very highly, inparticular in a driving situation (F_(n)) in which a target object isused for regulation.

Another possible indicator (I_(n)) is the average power of the signalsreceived by the sensor. If a high-power signal is received by a sensor,this may originate, for example, from individual strong targets or frommultiple weak targets. However, if there are no targets or if the sensoris soiled or has been blinded, then a low power level is received by thesensor. For the calculation of the indicator (I_(n)), the powers of allpeaks detected in the spectrum of the received signals are added up andan average is formed. Following this, it is also advisable for thisindicator (I_(n)) to be normalized to a value between 0 and 1 to permita comparison with the other indicators (I_(n)). This indicator (I_(n))is of great importance in particular when total blinding of the sensoris to be ascertained and in driving situations (F_(n)) in which notarget object has been selected for regulation but other target objectshave been detected. This indicator (I_(n)) is thus weighted especiallyheavily (a_(n)) in determination of the corresponding probabilities(V_(n)).

Another possible indicator (I_(n)) is the sum of all objects detected bythe system during a measurement. Normalization in a value range between0 and 1 is also advisable with this indicator (I_(n)). This indicator(I_(n)) has a high significance, in particular, in evaluation of thetotal blindness of the sensor and in driving situations (F_(n)) in whichno target object has been detected for regulation but other targetobjects are being detected. This indicator (I_(n)) is thus weightedhighly (a_(n)) accordingly in determination of the correspondingprobabilities (V_(n)).

Another possible indicator (I_(n)) is the linkage of the distance andamplitude of the object detected at the greatest distance. In this case,the system first determines the distance and amplitude of the targetobject detected at the greatest distance. Then these two values aremultiplied to obtain a quantity which is independent of the objectgeometry. After suitable normalization, preferably in a value rangebetween 0 and 1, this indicator (I_(n)) provides information regardingthe maximum range of the sensor and the signal strength of the detectedtargets in particular. If this indicator (I_(n)) drops below a certainlimit value for a longer period of time, it may be deduced that thesensor is blind. If no target object is detected, this indicator (I_(n))is set at 0. This indicator (I_(n)) is then weighted highly (a_(n)), inparticular when no direct regulation object is detected but other targetobjects are detected.

Another possible indicator (I_(n)) is the road surface reflectiondetected by the system. In this case, it is possible for the sensor todetect road surface reflection over its vertical detection range. Thisindicator (I_(n)) may be used in particular for evaluation when thereare no target and/or regulation objects. In these driving situations(F_(n)), this indicator (I_(n)) is accordingly weighted strongly(a_(n)). Since the road surface reflection detected by the system isusually detected only very weakly, soiling and/or blinding of the sensormay thus be detected very promptly. This may be ascertained by the factthat in the case of soiling and/or blinding, the weak signals of theroad reflection detected by the system no longer appear in the spectrumof all detected signals. Again with this indicator (I_(n)),normalization in a value range between 0 and 1 is suggested to make itcomparable to other indicators (I_(n)).

In general, all indicators (I_(n)) depend more or less on theenvironment structure. This fact must be taken into account in analysisof the corresponding signals in any case. However, to take thisinfluence into account with other influences, it is within the scope ofthe present invention that the signals are filtered and/or subjected tosome other signal processing in order to determine the indicators(I_(n)). The indicators (I_(n)) are preferably normalized to a valuerange between 0 and 1, but this may also be accomplished by some othermethod.

In general, the method presented here may be adapted to various vehiclesand/or sensor systems and/or environment structures. To do so, thenumber of indicators (I_(n)) used may be increased or reduced, thetype/selection of indicators (I_(n)) may be varied, the drivingsituation (F_(n)) to be differentiated may be varied, accordingly otherprobabilities (V_(n)) may be determined, different states (P) for thesystem may be determined accordingly for the various probabilities(V_(n)) determined and the weighting factors (a_(n)) may be adapted tothe different driving situations (F_(n)) and the probabilities (V_(n))to be determined. It is left up to the expertise of those skilled in theart here to adapt this method in a suitable manner.

FIG. 3 illustrates possible transition states between states (P), where15, 16 and 17 denote three possible states P₁, P₂ and P₃. Arrows 18, 19and 20 denote three possible transition states between states P₁, P₂ andP₃. For example, if a state P₁ indicates that the performance of thesystem is optimal, state P₂ indicates that the performance of the systemis not optimal, and state P₃ indicates that no functioning of the systemis possible, then transition 18 may occur, for example, due to heavysnowfall which causes the sensor to become easily clogged. Transition 20may occur, for example, due to the fact that one's own vehicle isdriving in the snow drift of a vehicle driving in front of it. Thesensor which was previously soiled may become further clogged to thepoint of blindness. Transition 19 may occur, for example, due to snowsludge being thrown onto the sensor at high speed when the sensor thatwas previously free becomes clogged immediately and directly. Theprevailing driving situation (F_(n)) must always be taken into accountwithin transition states 18, 19 and 20 because the usability of theindividual indicators (I_(n)) and/or the transition states depends to agreat extent on which situation prevails on the road or in theenvironment. It is within the context of the present invention to alsoinclude possible transition states in an analysis.

FIG. 4 shows a possibility of how the determined state (P) of the systemmay enter into a control which triggers a cleaning device 23 for thesensor. A previously determined state of sensor (P) 14 is relayed hereto a control unit 21. In general, control unit 21 also receives othersignals 22 which are necessary for controlling a cleaning device 23.Then a cleaning device 23 is triggered accordingly by control unit 21.Cleaning device 23 may be a device that works with water, a mechanicalcleaning device or a heater for the sensor 14. In the case of a heater,it is usually such that the heating is activated below a certaintemperature threshold. Because of certain weather and/or ambientsituations, for example, it may happen that a soiled state (P) of thesensor 14 is determined but the ambient temperature is above thetemperature threshold, and in this case the heater would not normally betriggered. Thus triggering of the heating may be performed in a timelymanner due to the determined state (P) which indicates soiling of thesensor in order to counteract this soiling.

What is claimed is:
 1. A method of state estimation in a system forperforming at least one of automatic longitudinal regulation andtransverse regulation in a motor vehicle, operating according to atleast one of radar principle and lidar principle, for detecting at leastone of soiling and blindness of a sensor, comprising the steps of:forming at least two indicators, wherein the indicators are formed fromat least one of signals received and transmitted by the sensor, andwherein the at least two indicators are weighted using weightingfactors; and performing state estimation taking into account the atleast two indicators.
 2. The method of state estimation according toclaim 1, wherein the weighted indicators are linked together.
 3. Themethod of state estimation according to claim 1, wherein sum of theweighting factors is not greater than one.
 4. The method of stateestimation according to claim 1, wherein the weighting factors depend onat least one of a driving situation and a probability which is to bedetermined.
 5. The method of state estimation according to claim 4,wherein the driving situation is at least one of: (a) the system detectsanother vehicle, which is used as a target object for regulation; (b)the system does not detect a possible target object, but the systemdetects other moving objects; and (c) the system does not detect apossible target object and any other moving object.
 6. The method ofstate estimation according to claim 4, wherein at least one of thefollowing probabilities is selected as the probability to be determined:(a) performance of the system is optimal; (b) performance of the systemis not optimal; and (c) no functioning of the system is possible.
 7. Themethod of state estimation according to claim 2, wherein the linkedindicators yield at least one probability which provides informationregarding a probable state of the system.
 8. The method of stateestimation according to claim 7, wherein the greatest of theprobabilities describes the state of the system.
 9. The method of stateestimation according to claim 1, wherein the indicators are normalized,such that the possible value varies in a range between 0 and
 1. 10. Themethod of state estimation according to claim 1, wherein at least one ofthe following indicators is used: (a) average angle quality of allobjects detected by the system, which provides information regardingquality of object angle determined; (b) object stability, whichdescribes rate of detection failures of at least one of target objectand regulation object selected for the longitudinal regulation of thevehicle; (c) average power of signals received by the sensor; (d) sum ofall objects detected by the system during a measurement; (e) linkage ofdistance and amplitude of an object detected at the greatest distance;and (f) road surface reflection detected by the system.
 11. The methodof state estimation according to claim 7, wherein a state is assumed tobe determined only when a result of the linked indicators is obtainedfor at least a predetermined period of time.
 12. The method of stateestimation according to claim 6, wherein the method further utilizestransition states existing among the probabilities and the states. 13.The method of state estimation according to claim 8, wherein thedetermined state of the system is entered into a control unit whichtriggers a cleaning device for the sensor.
 14. The method of stateestimation according to claim 13, wherein the cleaning device is atleast one of a device which works with water, a mechanical cleaningdevice and a heater for the sensor.
 15. A device for state estimation ina system for performing at least one of automatic longitudinalregulation and transverse regulation in a motor vehicle, operatingaccording to at least one of the radar principle and the lidarprinciple, for the detection of at least one of soiling and blindness ofa sensor, comprising: means for forming at least two indicators from atleast one of signals received and transmitted by the sensor; means forweighting the at least two indicators using weighting factors; and meansfor performing the state estimation by taking into account the at leasttwo indicators.